package sparkStreaming

import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.streaming.{Seconds, StreamingContext}

object StateDriver {

	def main(args: Array[String]) {

		// 定义更新状态方法，参数values为当前批次单词频度，state为以往批次单词频度
		val updateFunc = (values: Seq[Int], state: Option[Int]) => {
			val sum = values.sum
			val stateSum = state.getOrElse(0)
			Some(sum + stateSum)
		}

		val conf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount")
		val ssc = new StreamingContext(conf, Seconds(3))
//		ssc.checkpoint("hdfs://hadoop102:9000/streamCheck")
		ssc.checkpoint("cp")

		// Create a DStream that will connect to hostname:port, like hadoop102:9999
		val lines = ssc.socketTextStream("localhost", 9999)

		// Split each line into words
		val words = lines.flatMap(_.split(" "))

		//import org.apache.spark.streaming.StreamingContext._ // not necessary since Spark 1.3
		// Count each word in each batch
		val pairs = words.map(word => (word, 1))


		// 使用updateStateByKey来更新状态，统计从运行开始以来单词总的次数
		val stateDstream: DStream[(String, Int)] = pairs.updateStateByKey[Int](updateFunc)
		stateDstream.print()

//		val wordCounts = pairs.reduceByKey(_ + _)
//		wordCounts.print()

		ssc.start()             // Start the computation
		ssc.awaitTermination()  // Wait for the computation to terminate
		//ssc.stop()
	}

}

